class_docstr: Your Python model must use the same class name. Every Python model
  that is created must have "TritonPythonModel" as the class name.
docstr: `initialize` is called only once when the model is being loaded.
  Implementing `initialize` function is optional. This function allows
  the model to intialize any state associated with this model.
  Parameters
  ----------
  args : dict
    Both keys and values are strings. The dictionary keys and values are:
    * model_config: A JSON string containing the model configuration
    * model_instance_kind: A string containing model instance kind
    * model_instance_device_id: A string containing model instance device ID
    * model_repository: Model repository path
    * model_version: Model version
    * model_name: Model name
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code: def initialize(self, args):
      """`initialize` is called only once when the model is being loaded.
      Implementing `initialize` function is optional. This function allows
      the model to intialize any state associated with this model.
      Parameters
      ----------
      args : dict
        Both keys and values are strings. The dictionary keys and values are:
        * model_config: A JSON string containing the model configuration
        * model_instance_kind: A string containing model instance kind
        * model_instance_device_id: A string containing model instance device ID
        * model_repository: Model repository path
        * model_version: Model version
        * model_name: Model name
      """
      # You must parse model_config. JSON string is not parsed here
      self.model_config = json.loads(args["model_config"])
      print("model_config:", self.model_config)
  
      self.input_names = []
      for input_config in self.model_config["input"]:
          self.input_names.append(input_config["name"])
      print("postprocess input names:", self.input_names)
  
      self.output_names = []
      self.output_dtype = []
      for output_config in self.model_config["output"]:
          self.output_names.append(output_config["name"])
          dtype = pb_utils.triton_string_to_numpy(
              output_config["data_type"]
          )
          self.output_dtype.append(dtype)
      print("postprocess output names:", self.output_names)
  
      yaml_path = (
          os.path.abspath(os.path.dirname(__file__)) + "/deploy.yaml"
      )
      self.postprocess_ = fd.vision.segmentation.PaddleSegPostprocessor(
          yaml_path
      )
  
file_path: PaddleSeg/deploy/fastdeploy/semantic_segmentation/serving/fastdeploy_serving/models/postprocess/1/model.py
imports: import json
  import numpy
  import time
  import os
  import fastdeploy
  import triton_python_backend_utils
label_desc: Use image node_id 1 for calling the TritonPythonModel obj's underlying member method code with example usage: obj.initialize(args) without return types